code: def apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor):  # type: ignore
      # Compute adjusted learning-rate.
      r_ = r / (1 + t * decay_factor)
      # Add gradient of regularization term.
      g_regularized = norm_coefficient * x + g
      # Update squared accumulated gradient.
      h_new = h + g_regularized * g_regularized
      # Compute ADAGRAD's gradient scaling factors
      h_sqrt = np.sqrt(h_new) + epsilon
      # Apply ADAGRAD update rule.
      x_new = x - r_ * g_regularized / h_sqrt
      return (x_new, h_new)
  
file_path: onnx/onnx/backend/test/case/node/adagrad.py
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
  from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
label_desc: Use image node_id 1 for calling a global function with example usage: apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor) and returns: x_new,  h_new